An image is composed of three signals of R, G, and B, and image split extracts the same color area or the same object area in the image composed of input color signals. Such image extraction data may be useful in fields related to digital image processing such as image analysis and image recognition for object based image information processing.
Most image area split methods based on color signals usually project input color signals to different types of color spaces and extract their density or split them using space adjacency of each color pixel in the image area.
More specifically, the image split method includes edge detection split, threshold based split, area based split, and split method using movement. In particular, area growth techniques detect a seed point serving as a reference of the area through an area-based image split method and split areas through a method of determining whether neighboring pixels may be included in the same area as the seed point according to the relationship between the seed point and the neighboring pixels.
However, a major concern with image split is over split of the area. Over split means that the image is split too much into the unintended area, and it is necessary to set appropriate criteria for area growth and merging to prevent such over split. At this time, consideration should also be given to the size of the split area.
Splitting an image through the area growth technique may be comparatively superior in performance and may be implemented simply. However, it is difficult to set a precise split criterion for progressive changes in intensity of light, such as shadows in real photographs, and it is much affected by noise.
In particular, the conventional area growth technique using a single frame is vulnerable to noise. In order to reduce the noise, a method of applying a smoothing operation is used. However, this has the effect of reducing noise in a single image, while sharp shapes are wasted, resulting in deterioration of image split performance.